Abstract
While human trafficking often conjures up images of victims being taken by force, in reality, a minority of today’s slave population are physically abducted. Rather, a significant share of human trafficking victims are “pushed” (e.g., trying to escape crisis conditions) or “pulled” (e.g., pursuing the prospect of economic opportunities) into situations of high risk. This study focuses on those who are “pulled” into risky scenarios, assessing when individuals make decisions that may put themselves at risk. I assume that individuals are boundedly rational, and propose an aspiration-based model of decision-making, which predicts that increased salience in relative deprivation can lead individuals to be more risk-seeking, putting themselves and their children at greater risk for exploitation. Using both an original survey experiment and nationally-representative data in Nepal, I find that consistent with the theoretical model, perceptions of relative deprivation induce more risk-seeking behavior. This result speaks to the interaction between inequality and risk tolerance, and how economic and social forces that alter perceived relative deprivation can increase vulnerability to exploitation.
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Notes
In 1981, Mauritania became the last country in the world to legally abolish slavery (Sutter 2012).
Given that human trafficking is in the shadows, this is a rough estimate at best. At this time, comprehensive and accurate numbers for the extent of human trafficking worldwide do not exist.
The International Agreement for the Suppression of the White Slave Traffic was adopted on May 18, 1904, and entered into force on July 18, 1905.
The International Convention for the Suppression of the White Slave Traffic was adopted on May 4, 1910.
The connection between human trafficking and slavery is described in detail by Koettl (2009).
As of 2015, the Palermo Protocol was ratified by some 169 state. For up to date ratification status information, go to https://www.unodc.org/unodc/en/treaties/CTOC/countrylist-traffickingprotocol.html.
Men and boys represent nearly half of human trafficking victims (ILO 2012).
For example, consider the 2014 case of the Indian domestic worker Sangeeta Richards. She was found to be abused by Indian diplomat to the United States Devyani Khobragade, paying her a little over USD 1 an hour, withholding her passport, and using intimidation against her family in India. She was represented by anti-human trafficking group Safe Horizon and encouraged to apply for a T-1 visa, a special category of visas issued in the United States for human trafficking victims and qualifying family members (Scrutton 2014).
For example, consider Nepali and Indian migrant workers in Qatar, who were among the two largest group of laborers in Qatar in the World Cup infrastructure projects. Investigations have found that there have been cases of forced labor or human trafficking, where wages were not being paid, documents (e.g., passports) were being confiscated, and food and water were being withheld. They were essentially being held in labor camps that restricted them from leaving (Pattison 2013; Stephenson 2015).
For example, consider a bonded laborer in India, who willingly went to a brick kiln to work with the promise of USD 400 advance. The advance became a USD 400 debt, he could not leave without the permission of the brick kiln owner, and he would not be told when, or if, he could ever pay off his debt. This form of labor is the most prevalent form of slavery in South Asia (Coorlim et al. 2013).
Victims of organ trafficking include those who agreed to sell their organs, as they usually agree under false pretenses, like being told their organ will grow back, not receiving the payment they were promised, and/or not being provided with proper health care after surgery (Pokharel 2015).
For example, migrant long-term care givers paid an average of USD 5398 in recruitment fees to work in Israel, and migrant Nepali security workers paid an average of USD 4078 in recruitment fees to work in the United Arab Emirates (Verité 2013), which a person living under the poverty line (USD 2 per day) would not be able to afford.
I am agnostic on who is in one’s “aspiration window” in this study, which neither impacts the theoretical claim or empirical argument; however, I direct interested readers to the rich literature in behavioral marketing, psychology and sociology that discusses structural factors (e.g., physical proximity) that make social comparison more likely (King and Summers 1967). Jones and Gerard (1967) discusses that one is more likely to compare oneself with an individual (or group) who is “at about the same level” on given attributes (e.g., education, employment sector, ethnicity, age, etc.) than with an individual who is either greatly superior to or greatly inferior to oneself on the given attributes.
For further discussion of this issue see (Heath et al. 1999 p. 104).
It is worth noting that these employment agencies are not unique to Nepal. For instance, the Deputy Director of EUROPOL noted that deceived victims in Europe have often been recruited “through seemingly legitimate employment agencies or brokerages, and once they arrive in the Member States they are forced into prostitution” (Bruggeman 2002).
See the Nepal ISRC Factbook for district-level characteristics. 10 Village Development Committees (VDC) were randomly selected in Makwanpur and 13 VDCs were randomly selected in Bara. Note that VDCs are Nepal’s local-level rural administrative units, similar to counties in the United States. There are over 3000 VDCs across Nepal’s 75 districts.
The exchange rate noted here are based upon the exchange rate and wage levels during the time of data collection.
The experimental condition received human subjects approval, and was employed as it is a subtle rather than strong prime of relative poverty without any known long-lasting effects.
One winner was randomly chosen by the two NGO’s, and money was disbursed to the winner a month after data collection. The lottery activity was near the end of the survey because I wanted subjects to “earn” the money they would need to forego to participate in the lottery. This was important to reduce house money effects—the propensity for people to consume or risk money that they have received as a result of a windfall (Ackert et al. 2006; Henderson and Peterson 1992; Thaler and Johnston 1990). Moreover, for ethical considerations, I did not want participants to spend their own income. As 42% of respondents purchased one ticket (retaining 80% of their honorarium for participating in the survey) because of the novelty of the lottery, I deem participation to equal purchasing two or more tickets. Many respondents reported that they wanted to purchase one ticket out of curiosity. Given the novelty issue, participation is equated to purchasing more than one ticket (note that people were allowed to purchase up to five tickets).
Question wording: “Have you participated in a lottery before?” (Response Options: “No,” “Yes, once,” “2–3 times,” and “More than three times”).
“How many families do you know that have migrated for work?” (Response Options: “11+ individuals,” “6–10 individuals,” “1–5 individuals,” “No one”).
Do you watch an international news channel such as CNN, BBC, MSNBC, STAR, etc.?” (Response Options: “Yes,” “No”).
Given that the question on household income was designed to be the treatment, a non-standard income question is employed.
Hindus and Buddhists make up over 90% of the sample, so other religions are treated as the omitted category in all analyses that consider demographic controls.
A dichotomous measure for whether was Tamang, an indigenous minority group in Nepal considered to be low caste, and particularly vulnerable to exploitation (non-Tamang is the omitted category), was used as a measure for caste.
In a study based in the United States, Haisley et al. (2008) found that participants were more likely to purchase lottery tickets when their relative poverty was made salient using the same priming technique I employ in this study. The fact that we see similar findings in two very different contexts speaks to the external validity of the finding.
When using a logistic regression model (see Online Appendix B), the effect of relative deprivation prime on risk tolerance with respect to all three outcome measures is similar to what we see in the linear probability model. When adjusting for a battery of controls, the effect of the relative deprivation prime on an individual’s preference for the risky job option for oneself and one’s child is 5.48% (P = 0.07; see column (2) of Table B.3) and 7.13% (P = 0.001; see column (4) of Table B.3), respectively. The effect of the relative deprivation prime on lottery participation is 4.26% (P = 0.08; column (6) of Table B.3). Note that marginal effects were computed at the means of each of each of the control variables. As a robustness check, I assessed whether treatment effects differed depending on the interviewer, and found no such interaction was present.
I note “community” in this question to direct respondents to think about individuals that are physically or socially proximate, and hence, a relevant reference point for wealth. However, the exact reference group that individuals consider when thinking about their economic condition could be more narrow (e.g., friends) or more broad (e.g., other individuals of the same age). There were no respondents who selected the “Very Rich” option, and the summary statistics for this measure can be found in Table B.1 in Online Appendix B. Again, this measure was recoded to lie between 0 and 1 for all analyses reported in the here, allowing us to interpret all coefficient estimates in linear regressions as representing a \(100\beta\) percentage-point movement in the dependent variable.
One may be concerned that this pre-treatment relative wealth question may prime individuals to think about their relative income. However, respondents were not primed to think more about relative income than absolute income before the treatment (or control condition) was implemented, as a battery of questions on absolute wealth were asked between the relative wealth question and the experiment. But let us say that all respondents were primed to think about their relative position by this pre-treatment relative income question, and this pre-treatment question made salient the relative deprivation of those who are relatively poor. This is not a concern for two reasons. First, an equal subset of the control group and treatment group were potentially primed to feel relatively poor by the pre-treatment relative income question, and hence, just as many individuals in the control group were potentially primed as those in the treatment group (P = 0.82), so the potential bias is differenced out when comparing the control and treatment groups. 48.1% and 48.7% of individuals considered themselves to be relatively poor among the control group and treatment group, respectively. Second, and more importantly, if the question acted to make the relative deprivation of those who are relatively poor salient, the relatively poor individuals in the control group would have essentially been partially treated. If the control group were in fact partially treated, then the control group would not be a true control group, as they are more like the treatment group, thereby shrinking the possibility of detecting any difference between the control and treatment group. In other words, the direction of the bias would actually lead to a more conservative estimate of effect sizes.
While there are more lottery participants within the control group than in the treatment group among those who describe themselves as “very poor” and “somewhat poor” relative to others in their community, this pattern dramatically reverses among those who describe themselves as “slightly poor,” “neither rich nor poor,” “slightly rich,” “somewhat rich.” Recall that there were no subjects that considered themselves relatively “very rich” (see Fig. 3c).
The relative deprivation measure was standardized to be between 0 and 1.
Indeed, there is likely high levels of underreporting; however, there is no evidence that levels of underreporting systematically differ by district.
Panel analysis was not possible as the human trafficking incidence measure is not available annually.
Note that given there is no empirical research documenting the relationship between inequality and human trafficking specifically, it is not clear that a squared term is necessary when modeling the determinants of human trafficking prevalence.
The measure was on a three-point scale: high, medium, and low. For greater information on the classification and a map of conflict-affectedness, see http://www.satp.org/satporgtp/countries/nepal/database/conflictmap.htm.
NLSS defines literacy rates as the percent of the population aged 6 years and older who are literate. Being literate is defined as those who answer “Yes” to the following two questions: (1) Can you read a letter? (2) Can you write a letter?
The detailed methodology of constructing price indexes and poverty lines is provided in “Poverty Trends In Nepal (1995–96 and 2003–04),” His Majesty’s Government of Nepal, National Planning Commission Secretariat, Central Bureau of Statistics, September, 2005, available from the Nepal CBS or from the World Bank. Some district data was pulled from Sharma et al. (2007), which is based upon the NLSS dataset.
The three three outlier cases with relative deprivation measures that exceed 0.5 were dropped in this initial scatterplot analysis to ensure that the correlation is not driven by outlier cases.
Table B.6 in Online Appendix B presents ordered logistic regression results. Significance of the relationship between relative deprivation and human trafficking levels remain significant.
Note that relative deprivation is highly correlated to average income, as relative deprivation is defined as \(\mu G,\) where G is the Gini coefficient and \(\mu\) is the income of each individual if society were egalitarian, which translates to the average income in the region. Unsurprisingly, the partial \(R^2\) is nearly one when the average income in a district is used to predict the variation in relative deprivation at the district-level when relative deprivation is measured as \(\mu G.\) As such, for this exercise, the Gini coefficient acted as the measure of relative deprivation. Note that the implications of my analyses (displayed in Table 3) are substantively identical if I use G instead of \(\mu G\) as my measure of relative deprivation.
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Acknowledgments
This research was supported by a Stanford Interdisciplinary Graduate Presidential Fellowship, a Michelle R. Clayman Institute of Gender Dissertation Fellowship, a Stanford University Center on Philanthropy and Civil Society research grant, a PLAN International research grant, a Stanford GSB William Miller Research Fund grant, a Stanford Community Engagement grant, and a Stanford GSB Human Subjects research grant. A debt of gratitude goes to Jonathan Bendor, Jon Krosnick, David Laitin, Neil Malhotra, William Mishler, Elizabeth Zechmeister, and Cathy Zimmerman for their guidance and support. Kirin Jessel, Shreya Paudel, Oasis Paudel, and Prajwol Shakya served as excellent research assistants. The GSB Behavioral Lab, Himalayan Human Rights International, and Plan Nepal were invaluable to my efforts in data collection. I also appreciate the helpful comments and suggestions from the participants and discussants at the Annual American Political Science Association and Midwest Political Science Association Meetings, as well as political science workshops at UCLA, University of Southern California, University of Pennsylvania, and Vanderbilt University. All errors and opinions are my own. The replication code and data files to reproduce the results in this study are available at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DY1282.
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Mo, C.H. Perceived Relative Deprivation and Risk: An Aspiration-Based Model of Human Trafficking Vulnerability. Polit Behav 40, 247–277 (2018). https://doi.org/10.1007/s11109-017-9401-0
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DOI: https://doi.org/10.1007/s11109-017-9401-0